Learning Deep Image Priors for Blind Image Denoising

@article{Hou2019LearningDI,
  title={Learning Deep Image Priors for Blind Image Denoising},
  author={Xianxu Hou and Hongming Luo and Jingxin Liu and Bolei Xu and Ke Sun and Yuanhao Gong and Bozhi Liu and Guoping Qiu},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2019},
  pages={1738-1747}
}
  • Xianxu Hou, Hongming Luo, G. Qiu
  • Published 1 June 2019
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realistic domain. In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment. We tackle the domain alignment on two levels. 1) the feature-level prior is to learn domain-invariant features for corrupted images with different level noise; 2) the pixel… 

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